Authors

Abstract

Performance, power, and energy (PPE) are critical aspects of modern computing. It is challenging to
accurately predict, in real time, the effect of dynamic voltage and frequency scaling (DVFS) on PPE across
a wide range of voltages and frequencies. This results in the use of reactive, iterative, and inefficient
algorithms for dynamically finding good DVFS states. We propose PPEP, an online PPE prediction framework
that proactively and rapidly searches the DVFS space. PPEP uses hardware events to implement both a
cycles-per-instruction (CPI) model as well as a per-core power model in order to predict PPE across all DVFS
states.

We verify on modern AMD CPUs that the PPEP power model achieves an average error of 4.6% (2.8% standard
deviation) on 152 benchmark combinations at 5 distinct voltage-frequency states. Predicting average chip
power across different DVFS states achieves an average error of 4.2% with a 3.6% standard deviation.
Further, we demonstrate the usage of PPEP by creating and evaluating a highly responsive power capping
mechanism that can meet power targets in a single step. PPEP also provides insights for future development
of DVFS technologies. For example, we find that it is important to carefully consider background workloads
for DVFS policies and that enabling north bridge DVFS can offer up to 20% additional energy saving or a
1.4x performance improvement.